Data augmentation for very small image datasets
In my opinions, you have around $25\times 20 \times 2 + 5 \times 20 = 1100$ samples, so the list of problems is:

Lack of data
Imbalance between class 1,2 and class 3

With the simple task, the model with the low capacity (small number of parameters) is more suitable for your task and to avoid overfitting because of the lack of data. Since the augmentation is nothing but regularization, based on EfficientNetV2 paper, with the small model you don't need too much regularization, select some basic affine transformation and elastic transformation then apply Rand Augment with small magnitude mix with 0.1 dropout should be enough.
The second problem should be solved with the weight class for weight, which means set the scale factor for the third class, or duplicated the samples of the third class then apply the strong affine transformation to it.